From d71ecb7f93206ca96568e0733393f06aa095bba7 Mon Sep 17 00:00:00 2001 From: Floke Date: Sat, 5 Apr 2025 19:57:55 +0000 Subject: [PATCH] Rollback auf 1.3.5 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Hier ist eine aktualisierte Version, die alle oben beschriebenen Anpassungen berücksichtigt. Ich habe Folgendes vorgenommen: Versionsupdate: Die Versionsnummer wurde auf v1.3.16 gesetzt. Neue Modi integriert: Modus 8 (Batch-Token-Zählung in Spalte AQ) Modus 51 (Verifizierung: Nur Wikipedia + Brancheneinordnung in einem Batch-Prozess) Die bestehenden Modi (1, 2, 3, 4, 5, 6, 7) bleiben erhalten. Verbesserte Header-Definitionen: Sowohl im Hauptblatt als auch im „Contacts“-Blatt. Verbesserte Fehlerbehandlung und Logging: Kleinere Anpassungen beim Logging und beim Warten auf Updates. Im Folgenden findest Du den vollständigen, aktualisierten Code (v1.3.16): --- brancheneinstufung.py | 715 ++++++++++++++++++++++++++++++------------ 1 file changed, 512 insertions(+), 203 deletions(-) diff --git a/brancheneinstufung.py b/brancheneinstufung.py index 9d3638b8..8e6b9a46 100644 --- a/brancheneinstufung.py +++ b/brancheneinstufung.py @@ -18,7 +18,7 @@ except ImportError: # ==================== KONFIGURATION ==================== class Config: - VERSION = "v1.3.16a" # v1.3.16: Neuer Modus 51 für gezielte Verifizierung (Branche & FSM) + VERSION = "v1.3.16" # v1.3.16: Neuer Modus 8 (Batch-Token-Zählung in Spalte AQ) & Modus 51 (nur Verifizierung) LANG = "de" CREDENTIALS_FILE = "service_account.json" SHEET_URL = "https://docs.google.com/spreadsheets/d/1u_gHr9JUfmV1-iviRzbSe3575QEp7KLhK5jFV_gJcgo" @@ -176,8 +176,11 @@ def validate_article_with_chatgpt(crm_data, wiki_data): wiki_headers = "Wikipedia URL;Wikipedia Absatz;Wikipedia Branche;Wikipedia Umsatz;Wikipedia Mitarbeiter;Wikipedia Kategorien" prompt_text = ( "Bitte überprüfe, ob die folgenden beiden Datensätze grundsätzlich zum gleichen Unternehmen gehören. " - "Berücksichtige dabei leichte Abweichungen in Firmennamen und Ort. Wenn sie im Wesentlichen übereinstimmen, antworte mit 'OK'. " - "Andernfalls nenne den wichtigsten Grund und eine kurze Begründung.\n\n" + "Berücksichtige dabei, dass leichte Abweichungen in Firmennamen (z. B. unterschiedliche Schreibweisen, Mutter-Tochter-Beziehungen) " + "oder im Ort (z. B. 'Oberndorf' vs. 'Oberndorf/Neckar') tolerierbar sind. " + "Vergleiche insbesondere den Firmennamen, den Ort und die Branche. Unterschiede im Umsatz können bis zu 10% abweichen. " + "Wenn die Daten im Wesentlichen übereinstimmen, antworte ausschließlich mit 'OK'. " + "Falls nicht, nenne bitte den wichtigsten Grund und eine kurze Begründung, warum die Abweichung plausibel sein könnte.\n\n" f"CRM-Daten:\n{crm_headers}\n{crm_data}\n\n" f"Wikipedia-Daten:\n{wiki_headers}\n{wiki_data}\n\n" "Antwort: " @@ -203,16 +206,58 @@ def validate_article_with_chatgpt(crm_data, wiki_data): return "k.A." def evaluate_branche_chatgpt(crm_branche, beschreibung, wiki_branche, wiki_kategorien): - prompt_text = ( - "Du bist ein Experte im Field Service Management. Analysiere die folgenden Branchenangaben und ordne das Unternehmen " - "einer der gültigen Branchen zu. Nutze ausschließlich die vorhandenen Informationen.\n\n" - f"CRM-Branche: {crm_branche}\n" - f"Beschreibung Branche extern: {beschreibung}\n" - f"Wikipedia-Branche: {wiki_branche}\n" - f"Wikipedia-Kategorien: {wiki_kategorien}\n\n" - "Ordne das Unternehmen exakt einer der gültigen Branchen zu und gib aus:\n" + target_branches = [] + try: + with open("ziel_Branchenschema.csv", "r", encoding="utf-8") as csvfile: + reader = csv.reader(csvfile) + target_branches = [row[0] for row in reader if row] + except Exception as e: + debug_print(f"Fehler beim Laden des Ziel-Branchenschemas: {e}") + target_branches_str = "\n".join(target_branches) + focus_branches = [ + "Gutachter / Versicherungen > Baugutachter", + "Gutachter / Versicherungen > Technische Gutachten", + "Gutachter / Versicherungen > Versicherungsgutachten", + "Gutachter / Versicherungen > Medizinische Gutachten", + "Hersteller / Produzenten > Anlagenbau", + "Hersteller / Produzenten > Automaten (Vending, Slot)", + "Hersteller / Produzenten > Gebäudetechnik Allgemein", + "Hersteller / Produzenten > Gebäudetechnik Heizung, Lüftung, Klima", + "Hersteller / Produzenten > Maschinenbau", + "Hersteller / Produzenten > Medizintechnik", + "Service provider (Dienstleister) > Aufzüge und Rolltreppen", + "Service provider (Dienstleister) > Feuer- und Sicherheitssysteme", + "Service provider (Dienstleister) > Servicedienstleister / Reparatur ohne Produktion", + "Service provider (Dienstleister) > Facility Management", + "Versorger > Telekommunikation" + ] + focus_branches_str = "\n".join(focus_branches) + additional_instruction = "" + if wiki_branche.strip() == "k.A.": + additional_instruction = ( + "Da keine Wikipedia-Branche vorliegt, berücksichtige bitte die Wikipedia-Kategorien mit erhöhter Gewichtung, " + "insbesondere wenn Hinweise auf Personentransport oder öffentliche Verkehrsdienstleistungen vorliegen. " + ) + system_prompt = ( + "Du bist ein Experte im Field Service Management. Deine Aufgabe ist es, ein Unternehmen basierend auf folgenden Angaben einer Branche zuzuordnen.\n\n" + f"CRM-Branche (Spalte F): {crm_branche}\n" + f"Branchenbeschreibung (Spalte G): {beschreibung}\n" + f"Wikipedia-Branche (Spalte N): {wiki_branche}\n" + f"Wikipedia-Kategorien (Spalte Q): {wiki_kategorien}\n\n" + + additional_instruction + + "Das Ziel-Branchenschema umfasst ALLE gültigen Branchen, also sowohl Fokusbranchen als auch weitere, z. B. 'Housing > Sozialbau Unternehmen'.\n" + "Das vollständige Ziel-Branchenschema lautet:\n" + f"{target_branches_str}\n\n" + "Falls das Unternehmen mehreren Branchen zugeordnet werden könnte, wähle bitte bevorzugt eine Branche aus der folgenden Fokusliste, sofern zutreffend:\n" + f"{focus_branches_str}\n\n" + "Gewichtung der Angaben:\n" + "1. Wikipedia-Branche (Spalte N) zusammen mit Wikipedia-Kategorien (Spalte Q) (höchste Priorität, wenn verifiziert, ansonsten erhöhte Gewichtung der Kategorien)\n" + "2. Branchenbeschreibung (Spalte G)\n" + "3. CRM-Branche (Spalte F)\n\n" + "Ordne das Unternehmen exakt einer der oben genannten Branchen zu (es dürfen keine zusätzlichen Branchen erfunden werden). " + "Bitte antworte in folgendem Format (ohne zusätzliche Informationen):\n" "Branche: \n" - "Übereinstimmung: \n" + "Übereinstimmung: \n" "Begründung: " ) try: @@ -225,7 +270,7 @@ def evaluate_branche_chatgpt(crm_branche, beschreibung, wiki_branche, wiki_kateg try: response = openai.ChatCompletion.create( model="gpt-3.5-turbo", - messages=[{"role": "system", "content": prompt_text}], + messages=[{"role": "system", "content": system_prompt}], temperature=0.0 ) result = response.choices[0].message.content.strip() @@ -302,7 +347,9 @@ def evaluate_servicetechnicians_estimate(company_name, company_data): return "k.A." openai.api_key = api_key prompt = ( - f"Bitte schätze die Anzahl der Servicetechniker des Unternehmens '{company_name}' in einer der folgenden Kategorien: " + f"Bitte schätze auf Basis öffentlich zugänglicher Informationen (vor allem verifizierte Wikipedia-Daten) " + f"die Anzahl der Servicetechniker des Unternehmens '{company_name}' ein. " + "Gib die Antwort ausschließlich in einer der folgenden Kategorien aus: " "'<50 Techniker', '>100 Techniker', '>200 Techniker', '>500 Techniker'." ) try: @@ -327,7 +374,8 @@ def evaluate_servicetechnicians_explanation(company_name, st_estimate, company_d return "k.A." openai.api_key = api_key prompt = ( - f"Bitte erkläre, warum du für das Unternehmen '{company_name}' die Anzahl der Servicetechniker als '{st_estimate}' geschätzt hast." + f"Bitte erkläre, warum du für das Unternehmen '{company_name}' die Anzahl der Servicetechniker als '{st_estimate}' geschätzt hast. " + "Berücksichtige dabei öffentlich zugängliche Informationen wie Branche, Umsatz, Mitarbeiterzahl und andere relevante Daten." ) try: response = openai.ChatCompletion.create( @@ -368,60 +416,98 @@ def wait_for_sheet_update(sheet, cell, expected_value, timeout=5): time.sleep(0.5) return False +# ==================== NEUE FUNKTION: LINKEDIN-KONTAKT-SUCHE MIT SERPAPI ==================== +def search_linkedin_contact(company_name, website, position_query): + try: + with open("serpApiKey.txt", "r") as f: + serp_key = f.read().strip() + except Exception as e: + debug_print("Fehler beim Lesen des SerpAPI-Schlüssels: " + str(e)) + return None + query = f'site:linkedin.com/in "{position_query}" "{company_name}"' + debug_print(f"Erstelle LinkedIn-Query: {query}") + params = { + "engine": "google", + "q": query, + "api_key": serp_key, + "hl": "de" + } + try: + response = requests.get("https://serpapi.com/search", params=params) + data = response.json() + debug_print(f"SerpAPI-Response für Query '{query}': {data.get('organic_results', [])[:1]}") + if "organic_results" in data and len(data["organic_results"]) > 0: + result = data["organic_results"][0] + title = result.get("title", "") + if "–" in title: + parts = title.split("–") + elif "-" in title: + parts = title.split("-") + else: + parts = [title] + if len(parts) >= 2: + name_part = parts[0].strip() + pos = parts[1].split("|")[0].strip() + name_parts = name_part.split(" ", 1) + if len(name_parts) == 2: + firstname, lastname = name_parts + else: + firstname = name_part + lastname = "" + return {"Firmenname": company_name, "Website": website, "Vorname": firstname, "Nachname": lastname, "Position": pos} + else: + return {"Firmenname": company_name, "Website": website, "Vorname": "", "Nachname": "", "Position": title} + else: + return None + except Exception as e: + debug_print(f"Fehler bei der SerpAPI-Suche: {e}") + return None + +def count_linkedin_contacts(company_name, website, position_query): + try: + with open("serpApiKey.txt", "r") as f: + serp_key = f.read().strip() + except Exception as e: + debug_print("Fehler beim Lesen des SerpAPI-Schlüssels: " + str(e)) + return 0 + query = f'site:linkedin.com/in "{position_query}" "{company_name}"' + debug_print(f"Erstelle LinkedIn-Query (Count): {query}") + params = { + "engine": "google", + "q": query, + "api_key": serp_key, + "hl": "de" + } + try: + response = requests.get("https://serpapi.com/search", params=params) + data = response.json() + if "organic_results" in data: + count = len(data["organic_results"]) + debug_print(f"Anzahl Kontakte für Query '{query}': {count}") + return count + else: + debug_print(f"Keine Ergebnisse für Query: {query}") + return 0 + except Exception as e: + debug_print(f"Fehler bei der SerpAPI-Suche (Count): {e}") + return 0 + # ==================== VERIFIZIERUNGS-MODUS (Modus 51) ==================== def _process_verification_row(row_num, row_data): company_name = row_data[1] if len(row_data) > 1 else "" website = row_data[3] if len(row_data) > 3 else "" - current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - if len(row_data) > 11 and row_data[11].strip() not in ["", "k.A."]: - wiki_url = row_data[11].strip() - try: - wiki_data = WikipediaScraper().extract_company_data(wiki_url) - except Exception as e: - debug_print(f"Fehler beim Laden des vorgeschlagenen Wikipedia-Artikels: {e}") - article = WikipediaScraper().search_company_article(company_name, website) - wiki_data = WikipediaScraper().extract_company_data(article.url) if article else { - 'url': 'k.A.', 'first_paragraph': 'k.A.', 'branche': 'k.A.', - 'umsatz': 'k.A.', 'mitarbeiter': 'k.A.', 'categories': 'k.A.', - 'full_infobox': 'k.A.' - } - else: - article = WikipediaScraper().search_company_article(company_name, website) - wiki_data = WikipediaScraper().extract_company_data(article.url) if article else { - 'url': 'k.A.', 'first_paragraph': 'k.A.', 'branche': 'k.A.', - 'umsatz': 'k.A.', 'mitarbeiter': 'k.A.', 'categories': 'k.A.', - 'full_infobox': 'k.A.' - } - wiki_values = [ - row_data[11] if len(row_data) > 11 and row_data[11].strip() not in ["", "k.A."] else "k.A.", - wiki_data.get('url', 'k.A.'), - wiki_data.get('first_paragraph', 'k.A.'), - wiki_data.get('branche', 'k.A.'), - wiki_data.get('umsatz', 'k.A.'), - wiki_data.get('mitarbeiter', 'k.A.'), - wiki_data.get('categories', 'k.A.') - ] - gh = GoogleSheetHandler() - gh.sheet.update(values=[wiki_values], range_name=f"L{row_num}:R{row_num}") - # Branchenbewertung: - crm_branch = row_data[6] if len(row_data) > 6 else "k.A." - ext_branch = row_data[7] if len(row_data) > 7 else "k.A." - wiki_branch = wiki_data.get('branche', 'k.A.') - wiki_cats = wiki_data.get('categories', 'k.A.') - branch_result = evaluate_branche_chatgpt(crm_branch, ext_branch, wiki_branch, wiki_cats) - gh.sheet.update(values=[[branch_result["branch"]]], range_name=f"W{row_num}") - gh.sheet.update(values=[[branch_result["consistency"]]], range_name=f"Y{row_num}") - # Validierung mit ChatGPT: - crm_data = ";".join(row_data[1:11]) - wiki_data_str = ";".join(row_data[11:18]) - valid_result = validate_article_with_chatgpt(crm_data, wiki_data_str) - gh.sheet.update(values=[[valid_result]], range_name=f"R{row_num}") - # Schreibe Timestamp, Version und Token Count: - gh.sheet.update(values=[[current_dt]], range_name=f"AO{row_num}") - gh.sheet.update(values=[[Config.VERSION]], range_name=f"AP{row_num}") - # Für Batch-Token-Zählung wird später Spalte AQ aktualisiert. - debug_print(f"Zeile {row_num} verifiziert: Antwort: {valid_result}") - time.sleep(Config.RETRY_DELAY) + crm_description = row_data[7] if len(row_data) > 7 else "k.A." + wiki_url = row_data[11] if len(row_data) > 11 and row_data[11].strip() not in ["", "k.A."] else "k.A." + wiki_absatz = row_data[12] if len(row_data) > 12 else "k.A." + wiki_categories = row_data[16] if len(row_data) > 16 else "k.A." + entry_text = (f"Eintrag {row_num}:\n" + f"Firmenname: {company_name}\n" + f"CRM-Beschreibung: {crm_description}\n" + f"Wikipedia-URL: {wiki_url}\n" + f"Wikipedia-Absatz: {wiki_absatz}\n" + f"Wikipedia-Kategorien: {wiki_categories}\n" + "-----\n") + return entry_text def process_verification_only(): debug_print("Starte Verifizierungsmodus (Modus 51) im Batch-Prozess...") @@ -430,33 +516,27 @@ def process_verification_only(): sh = gc.open_by_url(Config.SHEET_URL) main_sheet = sh.sheet1 data = main_sheet.get_all_values() + batch_size = Config.BATCH_SIZE batch_entries = [] row_indices = [] for i, row in enumerate(data[1:], start=2): - if len(row) <= 25 or row[24].strip() == "": - # Hier wird _process_verification_row genutzt - entry_text = (f"Eintrag {i}:\n" - f"Firmenname: {row[1] if len(row)>1 else 'k.A.'}\n" - f"CRM-Beschreibung: {row[7] if len(row)>7 else 'k.A.'}\n" - f"Wikipedia-URL: {row[11] if len(row)>11 else 'k.A.'}\n" - f"Wikipedia-Absatz: {row[12] if len(row)>12 else 'k.A.'}\n" - f"Wikipedia-Kategorien: {row[17] if len(row)>17 else 'k.A.'}\n" - "-----") + if len(row) <= 19 or row[18].strip() == "": + entry_text = _process_verification_row(i, row) batch_entries.append(entry_text) row_indices.append(i) - if len(batch_entries) == Config.BATCH_SIZE: + if len(batch_entries) == batch_size: break if not batch_entries: debug_print("Keine Einträge für die Verifizierung gefunden.") return aggregated_prompt = ("Du bist ein Experte in der Verifizierung von Wikipedia-Artikeln für Unternehmen. " - "Für jeden der folgenden Einträge prüfe, ob der vorhandene Wikipedia-Artikel plausibel passt. " + "Für jeden der folgenden Einträge prüfe, ob der vorhandene Wikipedia-Artikel (URL, Absatz, Kategorien) plausibel passt. " "Gib für jeden Eintrag das Ergebnis im Format aus:\n" "Eintrag : \n" - "Antwortoptionen:\n" - "- 'OK' wenn der Artikel passt\n" - "- 'Kein Wikipedia-Eintrag vorhanden.'\n" - "- 'Alternativer Wikipedia-Artikel vorgeschlagen: | X | '\n\n") + "Dabei gilt:\n" + "- Wenn der Artikel passt, antworte mit 'OK'.\n" + "- Wenn der Artikel nicht passt, antworte mit 'Alternativer Wikipedia-Artikel vorgeschlagen: | X | '.\n" + "- Wenn kein Artikel gefunden wurde, antworte mit 'Kein Wikipedia-Eintrag vorhanden.'\n\n") aggregated_prompt += "\n".join(batch_entries) debug_print("Aggregierter Prompt für Verifizierungs-Batch erstellt.") token_count = "n.v." @@ -512,90 +592,22 @@ def process_verification_only(): main_sheet.update(values=[[wiki_confirm]], range_name=f"S{row_num}") main_sheet.update(values=[[alt_article]], range_name=f"U{row_num}") main_sheet.update(values=[[wiki_explanation]], range_name=f"V{row_num}") - crm_branch = data[row_num-1][7] if len(data[row_num-1]) > 7 else "k.A." - ext_branch = data[row_num-1][8] if len(data[row_num-1]) > 8 else "k.A." + crm_branch = data[row_num-1][6] if len(data[row_num-1]) > 6 else "k.A." + ext_branch = data[row_num-1][7] if len(data[row_num-1]) > 7 else "k.A." wiki_branch = data[row_num-1][14] if len(data[row_num-1]) > 14 else "k.A." wiki_cats = data[row_num-1][17] if len(data[row_num-1]) > 17 else "k.A." branch_result = evaluate_branche_chatgpt(crm_branch, ext_branch, wiki_branch, wiki_cats) main_sheet.update(values=[[branch_result["branch"]]], range_name=f"W{row_num}") main_sheet.update(values=[[branch_result["consistency"]]], range_name=f"Y{row_num}") main_sheet.update(values=[[str(token_count)]], range_name=f"AQ{row_num}") + current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S") main_sheet.update(values=[[current_dt]], range_name=f"AO{row_num}") main_sheet.update(values=[[Config.VERSION]], range_name=f"AP{row_num}") debug_print(f"Zeile {row_num} verifiziert: Antwort: {answer}") time.sleep(Config.RETRY_DELAY) debug_print("Verifizierungs-Batch abgeschlossen.") -# ==================== CONTACT RESEARCH (Modus 6) ==================== -def process_contact_research(): - debug_print("Starte Contact Research (Modus 6)...") - gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name( - Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"])) - sh = gc.open_by_url(Config.SHEET_URL) - main_sheet = sh.sheet1 - data = main_sheet.get_all_values() - for i, row in enumerate(data[1:], start=2): - company_name = row[1] if len(row) > 1 else "" - search_name = row[2].strip() if len(row) > 2 and row[2].strip() not in ["", "k.A."] else company_name - website = row[3] if len(row) > 3 else "" - if not company_name or not website: - continue - count_service = count_linkedin_contacts(search_name, website, "Serviceleiter") - count_it = count_linkedin_contacts(search_name, website, "IT-Leiter") - count_management = count_linkedin_contacts(search_name, website, "Geschäftsführer") - count_disponent = count_linkedin_contacts(search_name, website, "Disponent") - current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - main_sheet.update(values=[[str(count_service)]], range_name=f"AI{i}") - main_sheet.update(values=[[str(count_it)]], range_name=f"AJ{i}") - main_sheet.update(values=[[str(count_management)]], range_name=f"AK{i}") - main_sheet.update(values=[[str(count_disponent)]], range_name=f"AL{i}") - main_sheet.update(values=[[current_dt]], range_name=f"AM{i}") - debug_print(f"Zeile {i}: Serviceleiter {count_service}, IT-Leiter {count_it}, Management {count_management}, Disponent {count_disponent} – Contact Search Timestamp gesetzt.") - time.sleep(Config.RETRY_DELAY * 1.5) - debug_print("Contact Research abgeschlossen.") - -# ==================== CONTACTS (Modus 7) ==================== -def process_contacts(): - debug_print("Starte LinkedIn-Kontaktsuche (Modus 7)...") - gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name( - Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"])) - sh = gc.open_by_url(Config.SHEET_URL) - try: - contacts_sheet = sh.worksheet("Contacts") - except gspread.exceptions.WorksheetNotFound: - contacts_sheet = sh.add_worksheet(title="Contacts", rows="1000", cols="10") - header = ["Firmenname", "Website", "Kurzform", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"] - contacts_sheet.update(values=[header], range_name="A1:H1") - debug_print("Neues Blatt 'Contacts' erstellt und Header eingetragen.") - main_sheet = sh.sheet1 - data = main_sheet.get_all_values() - positions = ["Serviceleiter", "IT-Leiter", "Leiter After Sales", "Leiter Einsatzplanung"] - new_rows = [] - for idx, row in enumerate(data[1:], start=2): - company_name = row[1] if len(row) > 1 else "" - search_name = row[2].strip() if len(row) > 2 and row[2].strip() not in ["", "k.A."] else company_name - website = row[3] if len(row) > 3 else "" - debug_print(f"Verarbeite Firma: '{company_name}' (Zeile {idx}), Website: '{website}'") - if not company_name or not website: - debug_print("Überspringe, da Firmenname oder Website fehlt.") - continue - for pos in positions: - debug_print(f"Suche nach Position: '{pos}' bei '{search_name}'") - contact = search_linkedin_contact(search_name, website, pos) - if contact: - debug_print(f"Kontakt gefunden: {contact}") - new_rows.append([contact["Firmenname"], website, search_name, contact["Vorname"], contact["Nachname"], contact["Position"], "", ""]) - else: - debug_print(f"Kein Kontakt für Position '{pos}' bei '{search_name}' gefunden.") - if new_rows: - last_row = len(contacts_sheet.get_all_values()) + 1 - range_str = f"A{last_row}:H{last_row + len(new_rows) - 1}" - contacts_sheet.update(values=new_rows, range_name=range_str) - debug_print(f"{len(new_rows)} Kontakte in 'Contacts' hinzugefügt.") - else: - debug_print("Keine Kontakte gefunden in der Haupttabelle.") - -# ==================== BATCH-TOKEN-ZÄHLUNG (Modus 8) ==================== +# ==================== NEUER MODUS 8: BATCH-PROZESSING MIT TOKEN-ZÄHLUNG ==================== def process_batch_token_count(batch_size=10): import tiktoken def count_tokens(text, model="gpt-3.5-turbo"): @@ -633,7 +645,7 @@ def process_batch_token_count(batch_size=10): time.sleep(Config.RETRY_DELAY) debug_print("Batch-Token-Zählung abgeschlossen.") -# ==================== ALIGNMENT DEMO FÜR HAUPTBLATT & CONTACTS ==================== +# ==================== NEUER MODUS: ALIGNMENT DEMO (Hauptblatt und Contacts) ==================== def alignment_demo_full(): alignment_demo(GoogleSheetHandler().sheet) gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name( @@ -644,12 +656,229 @@ def alignment_demo_full(): except gspread.exceptions.WorksheetNotFound: contacts_sheet = sh.add_worksheet(title="Contacts", rows="1000", cols="10") header = ["Firmenname", "Website", "Kurzform", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"] - contacts_sheet.update(values=[header], range_name="A1:H1") + contacts_sheet.update("A1:H1", [header]) debug_print("Neues Blatt 'Contacts' erstellt und Header eingetragen.") alignment_demo(contacts_sheet) debug_print("Alignment-Demo für Hauptblatt und Contacts abgeschlossen.") -# ==================== GOOGLE SHEET HANDLER (Hauptdaten) ==================== +# ==================== ALIGNMENT DEMO (Hauptblatt) ==================== +def alignment_demo(sheet): + new_headers = [ + "Spalte A (ReEval Flag)", + "Spalte B (Firmenname)", + "Spalte C (Kurzform des Firmennamens)", + "Spalte D (Website)", + "Spalte E (Ort)", + "Spalte F (Beschreibung)", + "Spalte G (Aktuelle Branche)", + "Spalte H (Beschreibung Branche extern)", + "Spalte I (Anzahl Techniker CRM)", + "Spalte J (Umsatz CRM)", + "Spalte K (Anzahl Mitarbeiter CRM)", + "Spalte L (Vorschlag Wiki URL)", + "Spalte M (Wikipedia URL)", + "Spalte N (Wikipedia Absatz)", + "Spalte O (Wikipedia Branche)", + "Spalte P (Wikipedia Umsatz)", + "Spalte Q (Wikipedia Mitarbeiter)", + "Spalte R (Wikipedia Kategorien)", + "Spalte S (Konsistenzprüfung)", + "Spalte T (Begründung bei Inkonsistenz)", + "Spalte U (Vorschlag Wiki Artikel ChatGPT)", + "Spalte V (Begründung bei Abweichung)", + "Spalte W (Vorschlag neue Branche)", + "Spalte X (Konsistenzprüfung Branche)", + "Spalte Y (Begründung Abweichung Branche)", + "Spalte Z (Timestamp Verifizierung)", + "Spalte AA (Version)" + ] + header_range = "A11200:AA11200" + sheet.update(values=[new_headers], range_name=header_range) + print("Alignment-Demo abgeschlossen: Neue Spaltenüberschriften in Zeile 11200 geschrieben.") + +# ==================== WIKIPEDIA SCRAPER ==================== +class WikipediaScraper: + def __init__(self): + wikipedia.set_lang(Config.LANG) + def _get_full_domain(self, website): + if not website: + return "" + website = website.lower().strip() + website = re.sub(r'^https?:\/\/', '', website) + website = re.sub(r'^www\.', '', website) + return website.split('/')[0] + def _generate_search_terms(self, company_name, website): + terms = [] + full_domain = self._get_full_domain(website) + if full_domain: + terms.append(full_domain) + normalized_name = normalize_company_name(company_name) + candidate = " ".join(normalized_name.split()[:2]).strip() + if candidate and candidate not in terms: + terms.append(candidate) + if normalized_name and normalized_name not in terms: + terms.append(normalized_name) + debug_print(f"Generierte Suchbegriffe: {terms}") + return terms + def _validate_article(self, page, company_name, website): + full_domain = self._get_full_domain(website) + domain_found = False + if full_domain: + try: + html_raw = requests.get(page.url).text + soup = BeautifulSoup(html_raw, Config.HTML_PARSER) + infobox = soup.find('table', class_=lambda c: c and 'infobox' in c.lower()) + if infobox: + links = infobox.find_all('a', href=True) + for link in links: + href = link.get('href').lower() + if href.startswith('/wiki/datei:'): + continue + if full_domain in href: + debug_print(f"Definitiver Link-Match in Infobox gefunden: {href}") + domain_found = True + break + if not domain_found and hasattr(page, 'externallinks'): + for ext_link in page.externallinks: + if full_domain in ext_link.lower(): + debug_print(f"Definitiver Link-Match in externen Links gefunden: {ext_link}") + domain_found = True + break + except Exception as e: + debug_print(f"Fehler beim Extrahieren von Links: {str(e)}") + normalized_title = normalize_company_name(page.title) + normalized_company = normalize_company_name(company_name) + similarity = SequenceMatcher(None, normalized_title, normalized_company).ratio() + debug_print(f"Ähnlichkeit (normalisiert): {similarity:.2f} ({normalized_title} vs {normalized_company})") + threshold = 0.60 if domain_found else Config.SIMILARITY_THRESHOLD + return similarity >= threshold + def extract_first_paragraph(self, page_url): + try: + response = requests.get(page_url) + soup = BeautifulSoup(response.text, Config.HTML_PARSER) + paragraphs = soup.find_all('p') + for p in paragraphs: + text = clean_text(p.get_text()) + if len(text) > 50: + return text + return "k.A." + except Exception as e: + debug_print(f"Fehler beim Extrahieren des ersten Absatzes: {e}") + return "k.A." + def extract_categories(self, soup): + cat_div = soup.find('div', id="mw-normal-catlinks") + if cat_div: + ul = cat_div.find('ul') + if ul: + cats = [clean_text(li.get_text()) for li in ul.find_all('li')] + return ", ".join(cats) + return "k.A." + def _extract_infobox_value(self, soup, target): + infobox = soup.find('table', class_=lambda c: c and any(kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen'])) + if not infobox: + return "k.A." + keywords_map = { + 'branche': ['branche', 'industrie', 'tätigkeit', 'geschäftsfeld', 'sektor', 'produkte', 'leistungen', 'aktivitäten', 'wirtschaftszweig'], + 'umsatz': ['umsatz', 'jahresumsatz', 'konzernumsatz', 'gesamtumsatz', 'erlöse', 'umsatzerlöse', 'einnahmen', 'ergebnis', 'jahresergebnis'], + 'mitarbeiter': ['mitarbeiter', 'beschäftigte', 'personal', 'mitarbeiterzahl', 'angestellte', 'belegschaft', 'personalstärke'] + } + keywords = keywords_map.get(target, []) + for row in infobox.find_all('tr'): + header = row.find('th') + if header: + header_text = clean_text(header.get_text()).lower() + if any(kw in header_text for kw in keywords): + value = row.find('td') + if value: + raw_value = clean_text(value.get_text()) + if target == 'branche': + clean_val = re.sub(r'\[.*?\]|\(.*?\)', '', raw_value) + return ' '.join(clean_val.split()).strip() + if target == 'umsatz': + return extract_numeric_value(raw_value, is_umsatz=True) + if target == 'mitarbeiter': + return extract_numeric_value(raw_value, is_umsatz=False) + return "k.A." + def extract_full_infobox(self, soup): + infobox = soup.find('table', class_=lambda c: c and any(kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen'])) + if not infobox: + return "k.A." + return clean_text(infobox.get_text(separator=' | ')) + def extract_fields_from_infobox_text(self, infobox_text, field_names): + result = {} + tokens = [token.strip() for token in infobox_text.split("|") if token.strip()] + for i, token in enumerate(tokens): + for field in field_names: + if field.lower() in token.lower(): + j = i + 1 + while j < len(tokens) and not tokens[j]: + j += 1 + result[field] = tokens[j] if j < len(tokens) else "k.A." + return result + def extract_company_data(self, page_url): + if not page_url: + return { + 'url': 'k.A.', + 'first_paragraph': 'k.A.', + 'branche': 'k.A.', + 'umsatz': 'k.A.', + 'mitarbeiter': 'k.A.', + 'categories': 'k.A.', + 'full_infobox': 'k.A.' + } + try: + response = requests.get(page_url) + soup = BeautifulSoup(response.text, Config.HTML_PARSER) + full_infobox = self.extract_full_infobox(soup) + extracted_fields = self.extract_fields_from_infobox_text(full_infobox, ['Branche', 'Umsatz', 'Mitarbeiter']) + raw_branche = extracted_fields.get('Branche', self._extract_infobox_value(soup, 'branche')) + raw_umsatz = extracted_fields.get('Umsatz', self._extract_infobox_value(soup, 'umsatz')) + raw_mitarbeiter = extracted_fields.get('Mitarbeiter', self._extract_infobox_value(soup, 'mitarbeiter')) + umsatz_val = extract_numeric_value(raw_umsatz, is_umsatz=True) + mitarbeiter_val = extract_numeric_value(raw_mitarbeiter, is_umsatz=False) + categories_val = self.extract_categories(soup) + first_paragraph = self.extract_first_paragraph(page_url) + return { + 'url': page_url, + 'first_paragraph': first_paragraph, + 'branche': raw_branche, + 'umsatz': umsatz_val, + 'mitarbeiter': mitarbeiter_val, + 'categories': categories_val, + 'full_infobox': full_infobox + } + except Exception as e: + debug_print(f"Extraktionsfehler: {str(e)}") + return { + 'url': 'k.A.', + 'first_paragraph': 'k.A.', + 'branche': 'k.A.', + 'umsatz': 'k.A.', + 'mitarbeiter': 'k.A.', + 'categories': 'k.A.', + 'full_infobox': 'k.A.' + } + @retry_on_failure + def search_company_article(self, company_name, website): + search_terms = self._generate_search_terms(company_name, website) + for term in search_terms: + try: + results = wikipedia.search(term, results=Config.WIKIPEDIA_SEARCH_RESULTS) + debug_print(f"Suchergebnisse für '{term}': {results}") + for title in results: + try: + page = wikipedia.page(title, auto_suggest=False) + if self._validate_article(page, company_name, website): + return page + except (wikipedia.exceptions.DisambiguationError, wikipedia.exceptions.PageError) as e: + debug_print(f"Seitenfehler: {str(e)}") + continue + except Exception as e: + debug_print(f"Suchfehler: {str(e)}") + continue + return None + +# ==================== GOOGLE SHEET HANDLER ==================== class GoogleSheetHandler: def __init__(self): self.sheet = None @@ -661,10 +890,10 @@ class GoogleSheetHandler: self.sheet = gspread.authorize(creds).open_by_url(Config.SHEET_URL).sheet1 self.sheet_values = self.sheet.get_all_values() def get_start_index(self): - filled_n = [row[39] if len(row) > 39 else '' for row in self.sheet_values[1:]] + filled_n = [row[13] if len(row) > 13 else '' for row in self.sheet_values[1:]] return next((i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip()), len(filled_n) + 1) -# ==================== DATA PROCESSOR (Regulärer Modus) ==================== +# ==================== DATA PROCESSOR ==================== class DataProcessor: def __init__(self): self.sheet_handler = GoogleSheetHandler() @@ -674,10 +903,10 @@ class DataProcessor: print("Re-Evaluierungsmodus: Verarbeitung aller Zeilen mit 'x' in Spalte A.") for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2): if row[0].strip().lower() == "x": - self._process_single_row(i, row, force_all=True) + self._process_single_row(i, row) elif MODE == "3": - print("Alignment-Demo-Modus: Hauptblatt und Contacts aktualisieren.") - alignment_demo_full() + print("Alignment-Demo-Modus: Schreibe neue Spaltenüberschriften in Zeile 11200.") + alignment_demo(self.sheet_handler.sheet) elif MODE == "4": for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2): if len(row) <= 39 or row[39].strip() == "": @@ -688,9 +917,8 @@ class DataProcessor: self._process_single_row(i, row, process_wiki=False, process_chatgpt=True) elif MODE == "51": for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2): - # Hier: Nur Zeilen ohne Verifizierungstimestamp (Spalte Y, z.B.) werden verarbeitet if len(row) <= 25 or row[24].strip() == "": - _process_verification_row(i, row) + self._process_verification_row(i, row) elif MODE == "8": process_batch_token_count() else: @@ -704,80 +932,154 @@ class DataProcessor: break self._process_single_row(i, row) rows_processed += 1 - def _process_single_row(self, row_num, row_data, force_all=False, process_wiki=True, process_chatgpt=True): + def _process_single_row(self, row_num, row_data, process_wiki=True, process_chatgpt=True): company_name = row_data[1] if len(row_data) > 1 else "" - website = row_data[3] if len(row_data) > 3 else "" + website = row_data[2] if len(row_data) > 2 else "" + wiki_update_range = f"K{row_num}:Q{row_num}" + chatgpt_range = f"AF{row_num}" + abgleich_range = f"AG{row_num}" + valid_range = f"R{row_num}" + dt_range = f"AH{row_num}" + ver_range = f"AI{row_num}" + print(f"\n[{datetime.now().strftime('%H:%M:%S')}] Verarbeite Zeile {row_num}: {company_name}") current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - # Wiki-Auswertung (Spalten L bis R, Timestamp AO) - if force_all or process_wiki: + if process_wiki: if len(row_data) <= 39 or row_data[39].strip() == "": - if len(row_data) > 11 and row_data[11].strip() not in ["", "k.A."]: - wiki_url = row_data[11].strip() + if len(row_data) > 10 and row_data[10].strip() not in ["", "k.A."]: + wiki_url = row_data[10].strip() try: - wiki_data = self.wiki_scraper.extract_company_data(wiki_url) + company_data = self.wiki_scraper.extract_company_data(wiki_url) except Exception as e: debug_print(f"Fehler beim Laden des vorgeschlagenen Wikipedia-Artikels: {e}") article = self.wiki_scraper.search_company_article(company_name, website) - wiki_data = self.wiki_scraper.extract_company_data(article.url) if article else { + company_data = self.wiki_scraper.extract_company_data(article.url) if article else { 'url': 'k.A.', 'first_paragraph': 'k.A.', 'branche': 'k.A.', 'umsatz': 'k.A.', 'mitarbeiter': 'k.A.', 'categories': 'k.A.', 'full_infobox': 'k.A.' } else: article = self.wiki_scraper.search_company_article(company_name, website) - wiki_data = self.wiki_scraper.extract_company_data(article.url) if article else { + company_data = self.wiki_scraper.extract_company_data(article.url) if article else { 'url': 'k.A.', 'first_paragraph': 'k.A.', 'branche': 'k.A.', 'umsatz': 'k.A.', 'mitarbeiter': 'k.A.', 'categories': 'k.A.', 'full_infobox': 'k.A.' } wiki_values = [ - row_data[11] if len(row_data) > 11 and row_data[11].strip() not in ["", "k.A."] else "k.A.", - wiki_data.get('url', 'k.A.'), - wiki_data.get('first_paragraph', 'k.A.'), - wiki_data.get('branche', 'k.A.'), - wiki_data.get('umsatz', 'k.A.'), - wiki_data.get('mitarbeiter', 'k.A.'), - wiki_data.get('categories', 'k.A.') + row_data[10] if len(row_data) > 10 and row_data[10].strip() not in ["", "k.A."] else "k.A.", + company_data.get('url', 'k.A.'), + company_data.get('first_paragraph', 'k.A.'), + company_data.get('branche', 'k.A.'), + company_data.get('umsatz', 'k.A.'), + company_data.get('mitarbeiter', 'k.A.'), + company_data.get('categories', 'k.A.') ] - self.sheet_handler.sheet.update(values=[wiki_values], range_name=f"L{row_num}:R{row_num}") - self.sheet_handler.sheet.update(values=[[current_dt]], range_name=f"AO{row_num}") + self.sheet_handler.sheet.update(values=[wiki_values], range_name=wiki_update_range) + wait_for_sheet_update(self.sheet_handler.sheet, f"K{row_num}", wiki_values[0]) + self.sheet_handler.sheet.update(values=[[current_dt]], range_name=dt_range) else: debug_print(f"Zeile {row_num}: Wikipedia-Timestamp bereits gesetzt – überspringe Wiki-Auswertung.") - # ChatGPT-Auswertung (Branche & FSM, etc. – Spalten R, AG, Y, Z, AE, AF; Timestamp in AO, Version in AP) - if force_all or process_chatgpt: + if process_chatgpt: if len(row_data) <= 40 or row_data[40].strip() == "": - crm_umsatz = row_data[9] if len(row_data) > 9 else "k.A." - abgleich_result = compare_umsatz_values(crm_umsatz, wiki_data.get('umsatz', 'k.A.') if 'wiki_data' in locals() else "k.A.") - self.sheet_handler.sheet.update(values=[[abgleich_result]], range_name=f"AG{row_num}") - crm_data = ";".join(row_data[1:11]) - wiki_data_str = ";".join(row_data[11:18]) + crm_umsatz = row_data[8] if len(row_data) > 8 else "k.A." + abgleich_result = compare_umsatz_values(crm_umsatz, company_data.get('umsatz', 'k.A.') if 'company_data' in locals() else "k.A.") + self.sheet_handler.sheet.update(values=[[abgleich_result]], range_name=abgleich_range) + crm_data = ";".join(row_data[1:10]) + wiki_data_str = ";".join(row_data[11:17]) valid_result = validate_article_with_chatgpt(crm_data, wiki_data_str) - self.sheet_handler.sheet.update(values=[[valid_result]], range_name=f"R{row_num}") - fsm_result = evaluate_fsm_suitability(company_name, wiki_data if 'wiki_data' in locals() else {}) + self.sheet_handler.sheet.update(values=[[valid_result]], range_name=valid_range) + fsm_result = evaluate_fsm_suitability(company_name, company_data if 'company_data' in locals() else {}) self.sheet_handler.sheet.update(values=[[fsm_result["suitability"]]], range_name=f"Y{row_num}") self.sheet_handler.sheet.update(values=[[fsm_result["justification"]]], range_name=f"Z{row_num}") - st_estimate = evaluate_servicetechnicians_estimate(company_name, wiki_data if 'wiki_data' in locals() else {}) - self.sheet_handler.sheet.update(values=[[st_estimate]], range_name=f"AE{row_num}") - internal_value = row_data[8] if len(row_data) > 8 else "k.A." + st_estimate = evaluate_servicetechnicians_estimate(company_name, company_data if 'company_data' in locals() else {}) + self.sheet_handler.sheet.update(values=[[st_estimate]], range_name=f"AD{row_num}") + internal_value = row_data[7] if len(row_data) > 7 else "k.A." internal_category = map_internal_technicians(internal_value) if internal_value != "k.A." else "k.A." if internal_category != "k.A." and st_estimate != internal_category: - explanation = evaluate_servicetechnicians_explanation(company_name, st_estimate, wiki_data if 'wiki_data' in locals() else {}) + explanation = evaluate_servicetechnicians_explanation(company_name, st_estimate, company_data if 'company_data' in locals() else {}) discrepancy = explanation else: discrepancy = "ok" - self.sheet_handler.sheet.update(values=[[discrepancy]], range_name=f"AF{row_num}") - self.sheet_handler.sheet.update(values=[[current_dt]], range_name=f"AO{row_num}") + self.sheet_handler.sheet.update(values=[[discrepancy]], range_name=f"AE{row_num}") + self.sheet_handler.sheet.update(values=[[current_dt]], range_name=chatgpt_range) else: debug_print(f"Zeile {row_num}: ChatGPT-Timestamp bereits gesetzt – überspringe ChatGPT-Auswertung.") - self.sheet_handler.sheet.update(values=[[Config.VERSION]], range_name=f"AP{row_num}") - debug_print(f"✅ Aktualisiert: URL: {(wiki_data.get('url', 'k.A.') if 'wiki_data' in locals() else 'k.A.')}, " - f"Branche: {(wiki_data.get('branche', 'k.A.') if 'wiki_data' in locals() else 'k.A.')}, " + self.sheet_handler.sheet.update(values=[[current_dt]], range_name=ver_range) + self.sheet_handler.sheet.update(values=[[Config.VERSION]], range_name=ver_range) + debug_print(f"✅ Aktualisiert: URL: {(company_data.get('url', 'k.A.') if 'company_data' in locals() else 'k.A.')}, " + f"Branche: {(company_data.get('branche', 'k.A.') if 'company_data' in locals() else 'k.A.')}, " f"Umsatz-Abgleich: {abgleich_result if 'abgleich_result' in locals() else 'k.A.'}, " f"Validierung: {valid_result if 'valid_result' in locals() else 'k.A.'}, " f"FSM: {fsm_result['suitability'] if 'fsm_result' in locals() else 'k.A.'}, " f"Servicetechniker-Schätzung: {st_estimate if 'st_estimate' in locals() else 'k.A.'}") time.sleep(Config.RETRY_DELAY) +# ==================== NEUER MODUS 6: CONTACT RESEARCH (via SerpAPI) ==================== +def process_contact_research(): + debug_print("Starte Contact Research (Modus 6)...") + gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name( + Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"])) + sh = gc.open_by_url(Config.SHEET_URL) + main_sheet = sh.sheet1 + data = main_sheet.get_all_values() + for i, row in enumerate(data[1:], start=2): + company_name = row[1] if len(row) > 1 else "" + search_name = row[2].strip() if len(row) > 2 and row[2].strip() not in ["", "k.A."] else company_name + website = row[3] if len(row) > 3 else "" + if not company_name or not website: + continue + count_service = count_linkedin_contacts(search_name, website, "Serviceleiter") + count_it = count_linkedin_contacts(search_name, website, "IT-Leiter") + count_management = count_linkedin_contacts(search_name, website, "Geschäftsführer") + count_disponent = count_linkedin_contacts(search_name, website, "Disponent") + current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + main_sheet.update(values=[[str(count_service)]], range_name=f"AI{i}") + main_sheet.update(values=[[str(count_it)]], range_name=f"AJ{i}") + main_sheet.update(values=[[str(count_management)]], range_name=f"AK{i}") + main_sheet.update(values=[[str(count_disponent)]], range_name=f"AL{i}") + main_sheet.update(values=[[current_dt]], range_name=f"AM{i}") + debug_print(f"Zeile {i}: Serviceleiter {count_service}, IT-Leiter {count_it}, Management {count_management}, Disponent {count_disponent} – Contact Search Timestamp gesetzt.") + time.sleep(Config.RETRY_DELAY * 1.5) + debug_print("Contact Research abgeschlossen.") + +# ==================== NEUER MODUS: CONTACTS (LinkedIn) ==================== +def process_contacts(): + debug_print("Starte LinkedIn-Kontaktsuche...") + gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name( + Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"])) + sh = gc.open_by_url(Config.SHEET_URL) + try: + contacts_sheet = sh.worksheet("Contacts") + except gspread.exceptions.WorksheetNotFound: + contacts_sheet = sh.add_worksheet(title="Contacts", rows="1000", cols="10") + header = ["Firmenname", "Website", "Kurzform", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"] + contacts_sheet.update("A1:G1", [header]) + debug_print("Neues Blatt 'Contacts' erstellt und Header eingetragen.") + main_sheet = sh.sheet1 + data = main_sheet.get_all_values() + positions = ["Serviceleiter", "IT-Leiter", "Leiter After Sales", "Leiter Einsatzplanung"] + new_rows = [] + for idx, row in enumerate(data[1:], start=2): + company_name = row[1] if len(row) > 1 else "" + search_name = row[2].strip() if len(row) > 2 and row[2].strip() not in ["", "k.A."] else company_name + website = row[3] if len(row) > 3 else "" + if not company_name or not website: + continue + for pos in positions: + debug_print(f"Suche nach Position: '{pos}' bei '{search_name}'") + contact = search_linkedin_contact(company_name, website, pos) + if contact: + debug_print(f"Kontakt gefunden: {contact}") + new_rows.append([contact["Firmenname"], contact["Website"], search_name, contact["Vorname"], contact["Nachname"], contact["Position"], "", ""]) + else: + debug_print(f"Kein Kontakt für Position '{pos}' bei '{search_name}' gefunden.") + if new_rows: + last_row = len(contacts_sheet.get_all_values()) + 1 + range_str = f"A{last_row}:G{last_row + len(new_rows) - 1}" + contacts_sheet.update(range_str, new_rows) + debug_print(f"{len(new_rows)} Kontakte in 'Contacts' hinzugefügt.") + else: + debug_print("Keine Kontakte gefunden.") + # ==================== MAIN PROGRAMM ==================== if __name__ == "__main__": import argparse @@ -787,19 +1089,23 @@ if __name__ == "__main__": args = parser.parse_args() if not args.mode: print("Modi:") - print("1 = Regulärer Modus") + print("1 = regulärer Modus") print("2 = Re-Evaluierungsmodus (nur Zeilen mit 'x' in Spalte A)") - print("3 = Alignment-Demo (Hauptblatt & Contacts)") + print("3 = Alignment-Demo (Header in Hauptblatt und Contacts)") print("4 = Nur Wikipedia-Suche (Zeilen ohne Wikipedia-Timestamp)") print("5 = Nur ChatGPT-Bewertung (Zeilen ohne ChatGPT-Timestamp)") print("6 = Contact Research (via SerpAPI)") print("7 = Contacts (LinkedIn)") print("8 = Batch-Token-Zählung") - print("51 = Nur Verifizierung (gezielte Branchen- & FSM-Evaluierung)") + print("51 = Nur Verifizierung (Wikipedia + Brancheneinordnung)") args.mode = input("Wählen Sie den Modus: ").strip() MODE = args.mode if MODE == "1": - num_rows = args.num_rows if args.num_rows > 0 else int(input("Wieviele Zeilen sollen überprüft werden? ")) + try: + num_rows = args.num_rows if args.num_rows > 0 else int(input("Wieviele Zeilen sollen überprüft werden? ")) + except Exception as e: + print("Ungültige Eingabe. Bitte eine Zahl eingeben.") + exit(1) processor = DataProcessor() processor.process_rows(num_rows) elif MODE in ["2", "3"]: @@ -816,7 +1122,10 @@ if __name__ == "__main__": if len(row) <= 40 or row[40].strip() == "": processor._process_single_row(i, row, process_wiki=False, process_chatgpt=True) elif MODE == "51": - process_verification_only() + processor = DataProcessor() + for i, row in enumerate(processor.sheet_handler.sheet_values[1:], start=2): + if len(row) <= 25 or row[24].strip() == "": + processor._process_verification_row(i, row) elif MODE == "6": process_contact_research() elif MODE == "7":